جست‌وجوی استاد و پژوهشگر

Christoph Uhrhanمشاهده پروفایل

استاد

• Robotics
• Flexible Automation
• Industrial Robotics
• +۴ مورد دیگر

Prof. Dr. sc. techn. Christoph Uhrhan is a faculty member at Furtwangen University , actively engaged in robotics research. His work focuses on robotics and flexible automation , particularly in industrial applications involving sheet metal bending , robot grippers , and human-robot interaction . With over 25 years of experience, his research spans tactile sensing, compliant mechanisms, and adaptive control systems. He maintains an office at Campus Furtwangen (Room: H 2.12) and can be contacted at Christoph.Uhrhan@hs-furtwangen.de . His research emphasizes user-oriented automation , combining passive and active features in robotic systems. Recent work includes object classification through force-sensitive fingertips using convolutional neural networks, while older contributions focus on mobile robotics and press brake automation. Key article trends show sustained focus on industrial robotics (1994-2022) with emphasis on tactile perception, compliant systems, and adaptive manufacturing. The work spans from foundational studies in sheet bending automation to modern CNN-based sensor interpretation.

Amar Hekaloمشاهده پروفایل

پژوهشگر

• Medical Image Processing
• Natural Language Processing (NLP)
• Information Extraction
• +۶ مورد دیگر

Amar Hekalo is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems (1992-2025) at the University of Würzburg , within the Faculty of Mathematics and Computer Science . Since November 2018, he has collaborated with the University Hospital of Würzburg to enhance medical documentation through speech/image processing , deep learning , and rule-based methods . Education: Master's degree in Physics His research focuses on automated image processing and NLP , covering information extraction , image classification , segmentation , object recognition , and medical applications . Recent work includes federated learning for private data reconstruction and AI applications in cardiac/radiological imaging. His publications reveal expertise in medical AI , particularly gastroenterology , cardiology , and radiology . He supervises Bachelor's and Master's students in AI applications for healthcare, logistics, and education. He contributes to open-source tools and actively seeks student assistants with Angular/NodeJS skills for radiology-focused documentation and e-learning tools.

• Computer Vision
• Document Analysis
• Optical Character Recognition
• +۳ مورد دیگر

Alexander Hartelt is a researcher at the Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Würzburg. He works at the Chair of Artificial Intelligence and Knowledge Systems (Computer Science VI) focusing on computer vision applications for historical document digitization. His research interests include: Computer Vision for historical document analysis Layout recognition and segmentation algorithms Optical character recognition (specializing in handwritten documents) Deep learning-based information extraction Digital preservation of cultural heritage materials Hartelt's recent publications demonstrate strong focus on medieval music manuscripts and historical print digitization. His work combines contour-based segmentation, deep learning networks, and open-source tool development to address challenges in historical document processing. Key application areas include music notation transcription and OCR for aged printed materials. Hartelt has been actively teaching since Winter Semester 2020/21, supervising: Artificial Intelligence I exercises Current Trends in Artificial Intelligence seminars Software internship topics His primary research projects include: Corpus Monodicum : Researching, transcribing and editing historically significant monodic music collections using the Ommr4all transcription tool Segmentation of Old Prints : Developing algorithms for segmentation and transcription of historical printed materials using pixel classifiers, contour-based approaches, and baseline detection methods

Wolfgang Echelmeyerمشاهده پروفایل

استاد

• Logistics and Supply Chain Management
• Material Handling
• Operations Research
• +۳ مورد دیگر

Professor Wolfgang Echelmeyer is a faculty member at Reutlingen University’s Faculty of Sustainability and Technology, specializing in Technical Logistics. His research focuses on logistics automation, human-robot interaction, and sustainable supply chain systems. Position: Professor Department: Technical Logistics Email: wolfgang.echelmeyer@reutlingen-university.de Research interests include robotic container unloading, lean logistics, and ergonomics in automated systems. Recent projects involve EU-funded initiatives like RobLog (EU# 270350) for cognitive robotics in freight handling. His work spans applications in city logistics, milk-run transportation systems, and decision support tools for automation investments. Key article trends explore robotics in logistics, sustainability in freight systems, and ergonomic resource planning. Publications from 2018–2024 emphasize innovative solutions for intermodal terminals, deformable cargo handling, and smart transportation networks. Administrative roles include Dean of Studies for the BSc in Sustainable Production and Business and Pro-Dean for Teaching at the Faculty of Sustainability and Technology. Collaborators include Marco Bonini, Tuan Nguyen, and Augusto Urru. Labs and teams: Active in projects like LogFabr (Logistics Fabrication) and partnerships with industry leaders such as Merck AG, Proctor & Gamble, and STIHL. His work addresses real-world challenges in logistics planning and automation deployment.

Jörn Ostermannمشاهده پروفایل

استاد

• Video Coding
• Computer Vision
• Machine Learning
• +۹ مورد دیگر

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Marcel Aachمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• High-Performance Computing
• Machine Learning
• +۳ مورد دیگر

Marcel Aach is a researcher at the Jülich Supercomputing Centre (JSC) within Research Centre Jülich, focusing on high-performance computing (HPC) and artificial intelligence (AI) integration. His work spans hyperparameter optimization, quantum-classical hybrid systems, and scalable deep learning architectures. Research Focus: AI-driven CFD simulations, medical imaging analysis, and edge AI optimization Infrastructure: Leverages JSC's HPC systems for large-scale computational tasks Recent publications highlight his contributions to resource-adaptive AI training, turbulent flow prediction using GRU models, and enhancing CT data accuracy for respiratory simulations. His work intersects HPC scalability, machine learning efficiency, and interdisciplinary applications. While no scientific awards are explicitly documented in the provided texts, his research demonstrates technical leadership in merging classical and quantum computing paradigms for AI optimization.

Chelsea Maria Johnمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Artificial Intelligence
• High-Performance Computing
• +۵ مورد دیگر

Chelsea Maria John is a Researcher at the Accelerating Devices Lab within the NOVAS division of the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. Her work focuses on high-performance computing and artificial intelligence, specializing in efficient machine learning model training, hardware accelerator benchmarking, and optimization strategies for communication and computation. Her research spans: Natural Language Processing and Large Language Models (LLMs) Scientific Machine Learning with Neural Operators High-Performance Computing system optimization Benchmarking frameworks for emerging AI hardware European open-source AI initiatives like OpenGPT-X Analysis of her 15 publications (2021-2025) reveals pioneering work in training LLMs on HPC systems, development of benchmarking suites (JUPITER, CARAML), and European LLM projects (Teuken series). Her research bridges AI and computational science, notably applying Fourier Neural Operators to fluid dynamics simulations. As part of the Accelerating Devices Lab, she contributes to advancing AI acceleration through hardware-software co-design, focusing on communication efficiency and computational optimization for next-generation scientific computing workloads.

Bastian Goldlückeمشاهده پروفایل

دانشیار

• Computer Vision
• Variational Methods
• Light Field Analysis
• +۳ مورد دیگر

Bastian Goldlücke serves as an Associate Professor in the Department of Computer Science at the University of Konstanz since 2014. His academic journey includes postdoctoral positions at the University of Heidelberg (2011), TU Munich (2010), and the University of Bonn (2009), following a Ph.D. from the Max Planck Institute for Computer Science in Saarbrücken (2006). His research centers on variational methods and their applications in computer vision, with specialized focus on light field analysis and inverse problems . Key contributions include novel frameworks for light field decomposition, real-time 3D reconstruction, and attention-based neural representations. His work bridges mathematical optimization with practical computer vision challenges in autonomous systems and 3D imaging. Notable scientific recognition includes: ERC Starting Grant 'Light Field Imaging and Analysis' (2013) Funding through SFB Transregio 'Quantitative Methods for Visual Computing' Prof. Goldlücke actively contributes to the academic community as a reviewer for major conferences and journals, co-organizer of the 'Light Fields for Computer Vision' workshops at ECCV 2014 and CVPR 2017, and area chair for ACCV 2014 and 3DV 2016. His current research is supported by significant European and German research grants focusing on computational imaging and visual analysis.

• Numerical Analysis
• Scientific Computing
• Mathematical Modeling
• +۱۲ مورد دیگر

Patricio Farrell is an applied mathematician and Research Group Leader at the Weierstrass Institute Berlin (WIAS), focusing on numerical analysis, scientific computing, and mathematical modeling for semiconductor devices. His work bridges applied analysis and practical applications, particularly through structure-preserving numerical methods for drift-diffusion systems and nonlinear PDEs. Key applications include next-generation semiconductors, perovskite photovoltaics, and neuromorphic computing. He actively collaborates with institutions like Inria Lille, the University of Oxford, and the Helmholtz Zentrum Berlin. Vice Chair of KOMSO (Committee for Mathematical Modeling, Simulation and Optimization) Editor of Open Mathematics Scientific Ambassador for Brain City Berlin Developed ChargeTransport.jl , an open-source Julia tool for semiconductor simulations used in academia and industry His research interests span numerical analysis, nonlinear PDEs, finite volume methods, and meshfree techniques, with applications in perovskites, nanowires, memristors, quantum wells, and laser design. He has secured ~€1.5M in third-party funding from organizations like the Leibniz Association and MATH+. Scientific Awards: Capital's Top 40 under 40

Dominik Fuchsgruberمشاهده پروفایل

پژوهشگر

• Uncertainty Estimation
• Spatio-Temporal Deep Learning
• Graph Neural Networks
• +۳ مورد دیگر

Dominik Fuchsgruber is a PhD Student at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science . His research focuses on uncertainty estimation in machine learning models and spatio-temporal deep learning applications. Educational background: M.Sc. and B.Sc. in Informatics from TUM, with an exchange semester at ETH Zürich Research experience: PhD work at TUM, internships at University of Alberta and Fraunhofer ESK Research Highlights : Developed energy-based uncertainty estimation frameworks for graph neural networks Created orientation-equivariant graph neural networks for edge signal analysis Contributions to efficient inference methods in large-scale machine learning Laboratory Affiliation : Part of the Data Analytics and Machine Learning group led by Prof. Dr. Stephan Günnemann at TUM.

Vladimir Golkovمشاهده پروفایل

پژوهشگر

• Deep Learning
• High-Dimensional Data
• Geometric Data
• +۶ مورد دیگر

Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.

Marten Lienenمشاهده پروفایل

پژوهشگر ارشد

• Generative Modeling
• Time Series Analysis
• Physics-Informed Machine Learning
• +۳ مورد دیگر

Marten Lienen is a Research Fellow at the Technical University of Munich, affiliated with the TUM School of Computation, Information and Technology in the Department of Computer Science (I26 - Data Analytics and Machine Learning). He works within Prof. Stephan Günnemann's research group located at Boltzmannstr. 3 in Garching. His research focuses on the intersection of generative modeling and physical systems simulation. Lienen specializes in developing machine learning approaches for time-dependent physical phenomena, particularly turbulent fluid flows. His work bridges deep learning with numerical methods for differential equations, creating novel frameworks that respect physical constraints while leveraging data-driven approaches. Analyzing his publication record reveals a clear trajectory toward building physics-informed generative models capable of simulating complex dynamical systems. His most recent work on UnHiPPO demonstrates sophisticated integration of uncertainty quantification with state space modeling, while his publications on fluid dynamics simulation showcase practical applications of generative ML to longstanding challenges in computational physics. Lienen maintains active online presences through his personal website (martenlienen.com), GitHub, and Twitter accounts, sharing research code and engaging with the machine learning community. His torchode library for PyTorch demonstrates commitment to creating practical tools for scientific machine learning.

Hao Xingمشاهده پروفایل

پژوهشگر

• Robot Vision
• Human Action Recognition
• Human-Object Interaction Recognition
• +۴ مورد دیگر

Hao Xing is a scientific researcher and post-doctoral fellow at the Institute for Cognitive Systems (ICS), Technical University of Munich (TUM), working with Prof. Gordon Cheng since May 2024. Previously, he served as a research assistant at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and completed his PhD under Prof. Darius Burschka from 2019. His educational background includes: Master of Science in Mechanical Engineering from Technical University of Munich Bachelor of Engineering in Mechanical Engineering from Hefei University of Technology, China Xing's research focuses on Robot Vision , Human Action Recognition , and Graph Convolutional Networks , with significant contributions to Human-Object Interaction Recognition , Scene Understanding , and Visual Depth Estimation . His work leverages machine learning to develop robust algorithms for human activity analysis and robotic perception, emphasizing spatio-temporal modeling and uncertainty handling in real-world scenarios. Analysis of his 13 recent publications (2019-2025) reveals a dominant focus on graph-based approaches for action recognition and segmentation, with increasing emphasis on open-world applications, uncertainty modeling, and healthcare integration. His research spans computer vision, robotics, and medical applications, demonstrating strong interdisciplinary impact through collaborations in surgical robotics and patient monitoring systems. Xing actively mentors students through master's thesis projects in Stereo Matching, Human Activity Segmentation, and Monocular Depth Estimation, requiring expertise in deep learning frameworks and computer vision algorithms. While no major grants are explicitly mentioned, his thesis topics indicate active research funding in geometric vision and human activity analysis. He operates within the Institute for Cognitive Systems (ICS) at TUM, a key unit in MIRMI's robotics ecosystem focused on advancing human-robot interaction through vision-based scene understanding and motion generation capabilities.

Christiane Hassenrückمشاهده پروفایل

پژوهشگر

• Molecular ecology
• Biodiversity
• Omics approaches
• +۴ مورد دیگر

Dr. Christiane Hassenrück is a Bioinformatician and Researcher at the Leibniz Institute for Baltic Sea Research Warnemünde (IOW) in Rostock, Germany, where she leads a working group focused on bioinformatics and 'omics data science. Her research integrates molecular ecology, biodiversity assessment, and computational approaches, with emphasis on FAIR data management, multivariate statistics, and environmental genomics. She previously held postdoctoral positions at MARUM (University of Bremen), the University of Copenhagen, the Leibniz Centre for Tropical Marine Research, and the Max Planck Institute for Marine Microbiology. Her work spans marine microbial ecology, biogeochemical cycling, and environmental bioinformatics. Key themes include the dynamics of microbial communities in upwelling systems, deep-sea hydrothermal vents, polar oceans, and polluted habitats (e.g., microplastics). She also develops methodologies for DNA recovery from challenging substrates and contributes to pathogen modeling (e.g., Vibrio vulnificus ) for ecosystem management. Her research leverages omics technologies to explore microbial adaptation, cryptic speciation, and nutrient limitation responses. Hassenrück collaborates on cross-disciplinary initiatives like the 2024 '10 Must-Knows from Biodiversity Research' and co-authors frameworks for research data management in Mecklenburg-Vorpommern. She maintains active profiles on ORCID, Google Scholar, ResearchGate, and GitHub to promote open science.

• Quantum Information
• Quantum Computing
• Quantum Communication
• +۵ مورد دیگر

Dr. Shahram Dehdashti is a researcher at the Chair of Theoretical Information Technology (Lehrstuhl für Theoretische Informationstechnik) at the Technical University of Munich (TUM), working under Prof. Boche. His research spans quantum information science, quantum communication, and quantum machine learning, with significant contributions to theoretical frameworks for future quantum networks and practical implementations in optical systems. His primary research domains include: Quantum Information and Communication Quantum Computing Algorithms Quantum Machine Learning Quantum Optics and Photonics Metamaterials and Transformation Optics Quantum Cognition He investigates quantum channel capacities, quantum-classical signal co-propagation, and quantum-enhanced feature spaces, while uniquely applying quantum probability models to cognitive decision-making processes. Analysis of his 2020-2025 publications reveals accelerating convergence between quantum computing and artificial intelligence, particularly in quantum federated learning for edge devices and quantum-inspired sentiment analysis. His work consistently bridges theoretical quantum information with practical optical implementations, while maintaining distinctive contributions to quantum cognition through experimental protocols for decision modeling. No scientific awards were documented in the source material. There is no evidence of student supervision or grant funding in the available information. Dr. Dehdashti operates within TUM's Chair of Theoretical Information Technology, which focuses on fundamental quantum information processing and theoretical aspects of next-generation communication systems.